The primary goal of this project is to reverse-engineer the interplay of gene variation (in candidate genes influencing renal tubular sodium transport and involved in renin-angiotensin-aldosterone system) and other variables contributing to the interindividual differences in blood pressure (BP) response to a thiazide diuretic. We will apply "pathway", or "systems biology", data analysis methods to the association study of BP response to a hydrochlorothiazide in 585 hypertensive individuals. Previously, they have been genotyped for genetic variation in 16 candidate genes, and measured for a number of intermediate phenotypes and other variables. A series of univariate analyses have been carried out, and a small number of statistically significant predictors of blood pressure response (and certain other intermediate phenotypes of interest) have been identified. However, the scope of these analyses was very limited. We intend to follow up with the multivariate analysis in order to gain the systemic understanding of the dataset and the underlying biological pathways. We intend to apply innovative multivariate analysis methods, predominantly Bayesian Network (BN) modeling and boosted classifiers / clusterers, to the data. Such data mining methods have been very successful when applied to the similar datasets in other domains. Finally, we will use this dataset to confirm, in the context of genetic epidemiology, the practical utility of BN modeling with respect to sensitivity and robustness. We will also investigate various technical aspects of BN reconstruction, as applied to the genetic data.